**2. ICPS non-medical software (ORI FIT-MET™)**

#### **2.1 Description of the process models**

Here we introduce ICPS ORI FIT-MET™ for the purpose to achieve fitness and prevent prediabetes, DM2 and complications such as CVD and AD. Uniquely, ICPS can construct trajectories of SV's (metrics) quasi real time in three domains of health: 1. Cardiometabolic Functioning and Disease (CMD), 2. Cardiovascular Functioning and Disease (CVD), and 3. Cardio-vegetative Functioning and Stress (CVS) with major implications to morbidity/mortality risks. Each of these domains have their mathematical process models to estimate the SV's (metrics). ICPS uses the predictive Kalman filter to predict future changes based on serially measured input data and using the respective predictive model calculation.

Ad 1. For CMD we use our Cardiometabolic Function Model (CMFM) which utilizes our Self-Adaptive Model of the Human Energy Metabolism (SAM-HEM) [1–4]; the Weight, Fat weight, Energy Balance model calculation (WFE); and the de novo lipogenesis, adaptive thermogenesis, and 24 hr. respiratory quotient model calculation WFE-DNL-AT [6]. The metric for insulin resistance in terms of R- or Rw-ratio carries the power of allowing to estimate the fat vs. carbohydrate burning and it is reflective of overall oxidative stress and inflammation. The CMFM modeling can calculate and predict the following physiological SV's: weight, fat mass, lean mass, ECW, ICW, R-ratio, Rw-ratio, Fat vs. Carbohydrate Oxidation, and 24 h non-protein respiratory quotient. With precise calorie counting the estimations of utilized macronutrient energy intake, de novo lipogenesis DNL and adaptive thermogenesis AT is possible.

Ad 2. For CVD process modeling ODI uses a cardiovascular fitness model (CVFM) in which the maximum oxygen uptake capacity (VO2max) is estimated

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

ment and recognize inadvertent side effects.

*and cardiovascular disease progression***.**

the daily macronutrient calorie intake.

to pharmacological interventions could be gauged, helping to track results of treat-

To our knowledge there is no noninvasive tool or monitoring device available to measure increased oxidative stress, inflammation, or insulin resistance in the user's natural environment. However, these pathological processes are strongly interlinked, leading to among others DM2, CMD, MS, CVD, ASCD, AD, Chronic Anemia of CKD, and HF. Importantly, ICPS is built on the holistic modeling approach of considering the entire human energy metabolism and insulin resistance. The latter can be viewed also as a surrogate marker for whole body oxidative stress and inflammation [15]. The bio-physical principle behind the proposed conceptual framework of ICPS and for process models is the recognition that the changes of the body composition (lean mass and fat mass) and the energy flow in and out of the body are governed by the fat vs. carbohydrate burning ratio and are strongly linked to insulin resistance [16, 17]. The significance of this is that an impaired mitochondrial lipid oxidation is a major anomaly in the chain of metabolic events leading to obesity and increase of insulin resistance [18]. High insulin resistance is associated with high respiratory quotient (RQ ) reflecting lower fat burning than normal [19]. We have no non-invasive measuring technique for Oxidative Stress. However, there is a strong connection between Oxidative Stress and Insulin Resistance [20]. Similarly, there are strong connections between inflammation and insulin resistance [21] but there is no non-invasive tool available currently to monitor whole body inflammation. Therefore, we intend to use the R- and Rw ratio to give at least a qualitative signal tool if the trends of changes in the metabolism are in the right or wrong direction in terms oxidative stress and inflammation. *Our central hypothesis is that by improving insulin resistance with the use of ICPS, we can ameliorate the condition of oxidative stress, overall inflammation, fat vs. carbohydrate oxidation,* 

To our knowledge ODI is the first in using the principle of "least action/ stationary action" as a principle for finding key physiological parameters of the energy metabolism [5, 6]. This is instrumental to estimate noninvasively the HOMA- IR linked marker of insulin resistance R- or Rw-ratio which are defined as R = ΔL/ΔF and Rw = ΔW/ΔF where ΔL, ΔW and ΔF are lean mass, weight and fat mass change over 24 hrs. For monitoring of insulin resistance, we were able to prove the feasibility of this concept [5–6]. Further, we have shown that our Weight, Fat weight, Energy Balance (WFE) model can estimate changes of Rw without mandatory calorie counting by serially measuring weight, fat weight, and energy balance [6]. Our extended model of WFE calculation is called WFE-DNL-AT [6] and allows also for estimating for the first time noninvasively in the user's natural environment the otherwise difficult or impossible to measure changes of state variables (SV's) of the metabolism such as 24 h nonprotein respiratory quotient (24hRQ ), utilized macronutrient energy intake, fat vs. carbohydrate oxidation rate (Fox/Cox), de novo lipogenesis (DNL), and adaptive thermogenesis (AT). However, WFE-DNL-AT calculations require knowledge of

For measuring daily changes of fat mass F, lean body mass L, the measurement of intracellular water mass (ICW) as well as extracellular water mass (ECW) are also needed. Unfortunately, bioimpedance measurement technologies are not suitable for clinical use in current form due to significant interindividual variations mainly due to lack of reliable bio-electrical modeling of electrical properties of a body segment. On the other hand, bioimpedance measurements are quite well suited for individualized measurements or serial measurement as the intraindividual variation is small. The electrical modeling issue can be improved with using the principle of "maximum information entropy" [9, 10]. Therefore, ODI developed

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from heart rate and measuring maximal activity energy expenditure (aEEmax) during graded exercise. The VO2max calculation model uses multiple linear regression with data on age, sex, height, percent body fat, aEEmax, and the slope between HR and physical activity as in [29]. CVFM is self-adapting (self-learning) from the daily incoming data and assesses changes of VO2max, exercise capacity, and heart rate reserve. We adopted the Critical Power model from [30] which is defined as the maximal sustainable aerobic power not causing "fatigue" to measure exercise capacity.

Ad 3. For CVS modeling ODI uses its Cardio-vegetative Stress Model (CVSM) which calculates the state variables (SV's) measuring functioning of the autonomous nervous system and estimating imbalance between sympathetic vs. parasympathetic activity. The time domain measure is the standard deviation of R-R intervals (SDNN) and the frequency domain power spectrum indicators are the low frequency spectral power of HRV (LFr), the high frequency spectral power of HRV (HFr), and their ratio LFr/HFr [12–13, 28].

#### **2.2 Data flow**

The usage of ICPS ORI FIT-MET™ is centered around data flowing in and out of the system. ICPS works with a wearable Sensor System (SS) to provide input data for the process models to arrive at metrics regarding CMD, CVD, CVS. The heart rate and physical activity energy expenditure related input data come from a wearable wristwatch-type fitness tracker like Garmin's smart watch. The body composition and hydration status related input data come from Garmin's Index scale. Alternatively, ODI developed its own fitness tracker, the sensor belt (SB) [7], and the BC-HS-A stand up scale [8, 9]. During regular use, ICPS updates every day the SV's and creates metrics allowing for trend prediction. The input and result data can be displayed on MHM or MST.

#### **2.3 Analysis and interpretation**

ODI's proposition is that MC modeling can provide special insight into physiological/ pathophysiological processes. MC modeling gives the expected direction of change of a variable in the future i.e. by connecting the data points and drawing a trajectory of the predicted changes. The benefit is that instead of comparing the user's data against a group average, the individualized modeling and data trajectory creation allows for self-comparison to historical data, capturing individual characteristics and facilitating individualized interventions. The MC models are generating metrics and trajectories allowing for tracking progress and facilitating dynamic behavioral changes. The undeniable advantage of modern portable electronics is that they can provide the resources and powerful data for self-healing in a non-judgmental way. The self-explaining context of SV's have the potential to raise self-awareness and draw attention to risk reduction and individual responsibility in the fight against modifiable noncommunicative disease processes. The derived metrics provided by ICPS have the potential to give the opportunity for education and learning about risks for health, development of new skills to fight risks, building motivation, as well as measuring self-efficacy in the fight against modifiable risks. The same ICPS metrics can be used by a personal trainer/primary provider for teaching and guiding needed changes of lifestyle or behavior. Importantly, it must be emphasized that the most important tool in our armamentarium to enhance insulin sensitivity and along with-it fat burning is endurance training [31] and it works even if no weight loss is achieved.

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*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

Inspired by the call for person-centered, outcome-driven treatment as a new paradigm for treatment of type 2 diabetes in primary care [14] we present here our vision of how MC type approaches could significantly help goals set forth by the academic authors in [14]. Target points for outcome in [14] are ASCD, CKD, and HF. For a practicing primary physician, it is desirable to offer non-invasive monitoring for patients in their natural environment not just for early detection of deterioration but also to improve patients' handling of rising issues with

Here we offer a preview about ICPS as a Medical Software and show how we can construct trajectories of SV's quasi real time in five domains of disease processes: 1. CMD and Metabolic Syndrome (MS), 2. Atherosclerotic Cardiovascular Disease (ASCD), 3. CVS and Autonomic Dysfunction (AD), 4. Chronic Anemia due to CKD, and 5. Heart failure (HF). It appears natural to extend the use of ICPS non-Medical Software with the areas of Chronic Anemia due to CKD and Heart failure.

Ad1. The MC model for CMD and MS remains the same Cardiometabolic Function Model (CMFM) as in ICPS ORI FIT-MET™. Response to the therapies of metabolic syndrome could be tracked and compared with baseline for de novo lipogenesis DNL, Fat vs. Carbohydrate Oxidation, and 24 h non-protein respiratory quotient. These metrics can supply valuable feedback in terms of ongoing diet and

Ad 2. For ASCD we want to extend CVFM. In the modeling of the maximum oxygen uptake capacity (VO2max) we also want to consider modeling oxygen delivery which depends on hemoglobin concentration (Hb), total hemoglobin mass due to chronic anemia of CKD and cardiac output. For modeling of oxygen delivery and oxygen consumption we use the model equations in [32]. For process modeling of hemoglobin concentration, total hemoglobin mass, and cardiac output see also

Ad 3. For CVS modeling ODI uses CVSM. For quantifying AD, the rationale is that there are strong associations between central adiposity (which is a marker of insulin resistance) and autonomic dysfunction [33] and there is an increased sympathetic system activity in metabolic syndrome [34]. We plan on using promising markers beyond SDNN, LFr, and HFr to recognize AD such as heart rate recovery time [33]. For the prediction of sudden cardiac death, we want to also use

Ad 4. We want to build a modeling platform for Chronic Anemia due to CKD. The main rationale is that anemia is a recognized risk factor for cardiovascular disease [36]. This is potentially important because iron deficiency anemia, if corrected, may in fact improve endothelial function and potentially improve morbidity and mortality [36]. Not surprisingly, anemia and insulin resistance and type 2 diabetes are interlinked [37] through various inflammatory processes which play crucial roles in the development of insulin resistance. There is also an inverse correlation between iron levels and HbA1c [38]. The reasons for this include kidney complications, neuropathy, and malabsorption occurring in the setting of advanced DM2. The elevated blood sugar will, over time, damage small blood vessels in the kidneys leading also to CKD. The erythropoietin production by the kidney goes down and along with it the production of red blood cells by bone marrow. Several studies show that diabetics with reduced renal function are more likely to end up with iron deficiency anemia than those without reduced

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

**3.1 Description of the process models**

The respective process models are the following:

exercise habits with implications to spur needed change.

the correlation dimension of R-R intervals D2 [35].

**3. ICPS medical software**

appropriate behaviors.

Ad 4. and 5.

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*
